Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Hu, Shengran"'
Researchers are investing substantial effort in developing powerful general-purpose agents, wherein Foundation Models are used as modules within agentic systems (e.g. Chain-of-Thought, Self-Reflection, Toolformer). However, the history of machine lea
Externí odkaz:
http://arxiv.org/abs/2408.08435
Go-Explore is a powerful family of algorithms designed to solve hard-exploration problems built on the principle of archiving discovered states, and iteratively returning to and exploring from the most promising states. This approach has led to super
Externí odkaz:
http://arxiv.org/abs/2405.15143
Autor:
Hu, Shengran, Clune, Jeff
Language is often considered a key aspect of human thinking, providing us with exceptional abilities to generalize, explore, plan, replan, and adapt to new situations. However, Reinforcement Learning (RL) agents are far from human-level performance i
Externí odkaz:
http://arxiv.org/abs/2306.00323
For the goal of automated design of high-performance deep convolutional neural networks (CNNs), Neural Architecture Search (NAS) methodology is becoming increasingly important for both academia and industries.Due to the costly stochastic gradient des
Externí odkaz:
http://arxiv.org/abs/2110.05242
In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries. Despite the steady stream of impressive empirical results, most existing NAS algorithms are computationally prohibitive to exec
Externí odkaz:
http://arxiv.org/abs/2011.13591
Publikováno v:
Complex & Intelligent Systems; Apr2023, Vol. 9 Issue 2, p1183-1192, 10p
